{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "pyMRST_gas_comp.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyPxH3R9IWz1TF/lmUroUQHs",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yohanesnuwara/pyMRST/blob/main/notebooks/pyMRST_gas_comp.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RXXDnZOgYG_Q"
      },
      "source": [
        "# Simulation of Compressible Gas Reservoir with PyMRST"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O3_fpJXZ8d0G",
        "outputId": "ca6d4dc3-bd00-4cf7-cc5a-0d435e88e56d"
      },
      "source": [
        "# Clone pyMRST\n",
        "!git clone https://github.com/yohanesnuwara/pyMRST"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'pyMRST'...\n",
            "remote: Enumerating objects: 287, done.\u001b[K\n",
            "remote: Counting objects: 100% (155/155), done.\u001b[K\n",
            "remote: Compressing objects: 100% (153/153), done.\u001b[K\n",
            "remote: Total 287 (delta 78), reused 0 (delta 0), pack-reused 132\u001b[K\n",
            "Receiving objects: 100% (287/287), 883.00 KiB | 7.68 MiB/s, done.\n",
            "Resolving deltas: 100% (124/124), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rqfpKzMp8zZk"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Add directory where you install PyMRST\n",
        "import sys\n",
        "sys.path.append(\"/content/pyMRST\") \n",
        "\n",
        "import pymrst\n",
        "from pymrst_units import *"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1OIu4smY9jqx"
      },
      "source": [
        "# Setup PyMRST (Takes about 3 minutes)\n",
        "pymrst.setup()"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iPcaUfjCvyMy"
      },
      "source": [
        "# Geometry\n",
        "nx, ny, nz = 30, 20, 10\n",
        "lx, ly, lz = 200, 200, 50\n",
        "\n",
        "# Reservoir property                                         \n",
        "# poro = dict({\"type\": \"heterogeneous\", \n",
        "#              \"field\": \"gaussian\", \n",
        "#              \"min\": 0.2, \"max\": 0.4, \"std\": 2.5})\n",
        "poro = dict({\"type\": \"homogeneous\", \n",
        "             \"value\": .3})\n",
        "\n",
        "# k = dict({\"type\": \"heterogeneous\", \n",
        "#           \"field\": \"kozeny\"})\n",
        "k = dict({\"type\": \"homogeneous\", \n",
        "          \"value\": 30*milli()*darcy()})\n",
        "\n",
        "ntg = dict({\"type\": \"heterogeneous\", \n",
        "            \"field\": \"lognorm\", \n",
        "            \"min\": 0.4, \"max\": 0.6})\n",
        "\n",
        "# Rock property\n",
        "rock = dict({\"c\": 1e-6*(1/1e+5), # Rock compressibility at ref pressure, 1/bar to 1/Pa\n",
        "             \"p_r\": 200*1e+5}) # Reference pressure, bar to Pa\n",
        "\n",
        "# Fluid property: 1-phase gas\n",
        "fluid = dict({\"type\": \"gas\", \n",
        "              \"mu0\": 5*centi()*poise(), # cp to Pa.s \n",
        "              \"rho_r\": 850, # Density at reference pressure\n",
        "              \"rhoS\": 750, # Density on the surface\n",
        "              \"p_r\": 200*barsa(), # Reference pressure, bar to Pa\n",
        "              \"c\": 1e-3/barsa(), # Fluid compressibility, 1/bar to 1/Pa\n",
        "              \"c_mu\": 2e-3/barsa()}) # Viscosity coefficient, 1/bar to 1/Pa  \n",
        "\n",
        "# Boundary \n",
        "bc_front = dict({\"type\": \"fluxside\", \"value\": 50 * 1.84e-6}) # bbl/d to m3/s\n",
        "bc_back = dict({\"type\": \"fluxside\", \"value\": 0})\n",
        "bc_left = dict({\"type\": \"fluxside\", \"value\": 100 * 1.1574e-5}) # m3/d to m3/s\n",
        "bc_right = dict({\"type\": \"pside\", \"value\": 100 * 1e+5}) # bar to Pa\n",
        "\n",
        "# Well\n",
        "# cell_loc1 = np.concatenate((np.arange(311,381+10,10), np.arange(411,481+10,10),\n",
        "#                             np.arange(412,482+10,10), np.arange(312,382+10,10), \n",
        "#                             np.array([491,391])))\n",
        "\n",
        "# well = dict({\"cell_loc\": [cell_loc1],\n",
        "#              \"type\": [\"bhp\"],\n",
        "#              \"value\": [100*1e5],\n",
        "#              \"radius\": [.1],\n",
        "#              \"skin\": [0],\n",
        "#              \"direction\": [\"y\"]})\n",
        "\n",
        "cell_loc1 = np.arange(10+60, nx*ny*nz, nx*ny)\n",
        "\n",
        "well = dict({\"cell_loc\": [cell_loc1], \n",
        "             \"type\": [\"bhp\"], \n",
        "             \"value\": [100*1e5], # bar to Pa \n",
        "             \"radius\": [.1],\n",
        "             \"skin\": [0], \n",
        "             \"direction\": [None]}) \n",
        "\n",
        "# Time step\n",
        "numSteps, totTime = 52, 365\n",
        "steps = [2, 5, 10, 20]"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RQbMl-wmUP4-"
      },
      "source": [
        "# Execute function\n",
        "pymrst.write_input(nx, ny, nz, lx, ly, lz, poro, k, rock, fluid, well, \n",
        "                   bc_front, bc_back, bc_left, bc_right, numSteps, totTime, steps)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nNGp4eb6VXlj",
        "outputId": "30bd3e8c-360a-4668-95e9-49f9a746cefc"
      },
      "source": [
        "# Execute simulator\n",
        "# Simulator results are added in new directory \"result_gas_1phase\"\n",
        "pymrst.gas_1phase()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "warning: function /content/mrst-core/utils/mex/md5sum.m shadows a core library function\n",
            "\n",
            "Time step 1: Time 0.00 -> 7.02 days\n",
            "  Iteration   1:  Res = 1.0021e+07\n",
            "  Iteration   2:  Res = 1.6682e-02\n",
            "  Iteration   3:  Res = 1.7289e-05\n",
            "  Iteration   4:  Res = 1.1053e-11\n",
            "\n",
            "Time step 2: Time 7.02 -> 14.04 days\n",
            "  Iteration   1:  Res = 6.9784e-02\n",
            "  Iteration   2:  Res = 2.5085e-04\n",
            "  Iteration   3:  Res = 2.1932e-09\n",
            "\n",
            "Time step 3: Time 14.04 -> 21.06 days\n",
            "  Iteration   1:  Res = 4.1683e-02\n",
            "  Iteration   2:  Res = 7.0261e-05\n",
            "  Iteration   3:  Res = 1.8724e-10\n",
            "\n",
            "Time step 4: Time 21.06 -> 28.08 days\n",
            "  Iteration   1:  Res = 3.3127e-02\n",
            "  Iteration   2:  Res = 4.3438e-05\n",
            "  Iteration   3:  Res = 7.9832e-11\n",
            "\n",
            "Time step 5: Time 28.08 -> 35.10 days\n",
            "  Iteration   1:  Res = 2.8698e-02\n",
            "  Iteration   2:  Res = 3.3181e-05\n",
            "  Iteration   3:  Res = 4.5054e-11\n",
            "\n",
            "Time step 6: Time 35.10 -> 42.12 days\n",
            "  Iteration   1:  Res = 2.6122e-02\n",
            "  Iteration   2:  Res = 2.8138e-05\n",
            "  Iteration   3:  Res = 3.2201e-11\n",
            "\n",
            "Time step 7: Time 42.12 -> 49.13 days\n",
            "  Iteration   1:  Res = 2.4505e-02\n",
            "  Iteration   2:  Res = 2.5315e-05\n",
            "  Iteration   3:  Res = 2.6501e-11\n",
            "\n",
            "Time step 8: Time 49.13 -> 56.15 days\n",
            "  Iteration   1:  Res = 2.3401e-02\n",
            "  Iteration   2:  Res = 2.3512e-05\n",
            "  Iteration   3:  Res = 2.3342e-11\n",
            "\n",
            "Time step 9: Time 56.15 -> 63.17 days\n",
            "  Iteration   1:  Res = 2.2573e-02\n",
            "  Iteration   2:  Res = 2.2193e-05\n",
            "  Iteration   3:  Res = 2.1170e-11\n",
            "\n",
            "Time step 10: Time 63.17 -> 70.19 days\n",
            "  Iteration   1:  Res = 2.1894e-02\n",
            "  Iteration   2:  Res = 2.1111e-05\n",
            "  Iteration   3:  Res = 1.9417e-11\n",
            "\n",
            "Time step 11: Time 70.19 -> 77.21 days\n",
            "  Iteration   1:  Res = 2.1299e-02\n",
            "  Iteration   2:  Res = 2.0150e-05\n",
            "  Iteration   3:  Res = 1.7867e-11\n",
            "\n",
            "Time step 12: Time 77.21 -> 84.23 days\n",
            "  Iteration   1:  Res = 2.0753e-02\n",
            "  Iteration   2:  Res = 1.9256e-05\n",
            "  Iteration   3:  Res = 1.6435e-11\n",
            "\n",
            "Time step 13: Time 84.23 -> 91.25 days\n",
            "  Iteration   1:  Res = 2.0236e-02\n",
            "  Iteration   2:  Res = 1.8404e-05\n",
            "  Iteration   3:  Res = 1.5094e-11\n",
            "\n",
            "Time step 14: Time 91.25 -> 98.27 days\n",
            "  Iteration   1:  Res = 1.9740e-02\n",
            "  Iteration   2:  Res = 1.7584e-05\n",
            "  Iteration   3:  Res = 1.3835e-11\n",
            "\n",
            "Time step 15: Time 98.27 -> 105.29 days\n",
            "  Iteration   1:  Res = 1.9258e-02\n",
            "  Iteration   2:  Res = 1.6793e-05\n",
            "  Iteration   3:  Res = 1.2656e-11\n",
            "\n",
            "Time step 16: Time 105.29 -> 112.31 days\n",
            "  Iteration   1:  Res = 1.8788e-02\n",
            "  Iteration   2:  Res = 1.6029e-05\n",
            "  Iteration   3:  Res = 1.1556e-11\n",
            "\n",
            "Time step 17: Time 112.31 -> 119.33 days\n",
            "  Iteration   1:  Res = 1.8329e-02\n",
            "  Iteration   2:  Res = 1.5292e-05\n",
            "  Iteration   3:  Res = 1.0536e-11\n",
            "\n",
            "Time step 18: Time 119.33 -> 126.35 days\n",
            "  Iteration   1:  Res = 1.7880e-02\n",
            "  Iteration   2:  Res = 1.4582e-05\n",
            "  Iteration   3:  Res = 9.5935e-12\n",
            "\n",
            "Time step 19: Time 126.35 -> 133.37 days\n",
            "  Iteration   1:  Res = 1.7441e-02\n",
            "  Iteration   2:  Res = 1.3900e-05\n",
            "  Iteration   3:  Res = 8.7254e-12\n",
            "\n",
            "Time step 20: Time 133.37 -> 140.38 days\n",
            "  Iteration   1:  Res = 1.7010e-02\n",
            "  Iteration   2:  Res = 1.3244e-05\n",
            "  Iteration   3:  Res = 7.9283e-12\n",
            "\n",
            "Time step 21: Time 140.38 -> 147.40 days\n",
            "  Iteration   1:  Res = 1.6589e-02\n",
            "  Iteration   2:  Res = 1.2615e-05\n",
            "  Iteration   3:  Res = 7.1981e-12\n",
            "\n",
            "Time step 22: Time 147.40 -> 154.42 days\n",
            "  Iteration   1:  Res = 1.6176e-02\n",
            "  Iteration   2:  Res = 1.2013e-05\n",
            "  Iteration   3:  Res = 6.5303e-12\n",
            "\n",
            "Time step 23: Time 154.42 -> 161.44 days\n",
            "  Iteration   1:  Res = 1.5773e-02\n",
            "  Iteration   2:  Res = 1.1436e-05\n",
            "  Iteration   3:  Res = 5.9208e-12\n",
            "\n",
            "Time step 24: Time 161.44 -> 168.46 days\n",
            "  Iteration   1:  Res = 1.5378e-02\n",
            "  Iteration   2:  Res = 1.0884e-05\n",
            "  Iteration   3:  Res = 5.3649e-12\n",
            "\n",
            "Time step 25: Time 168.46 -> 175.48 days\n",
            "  Iteration   1:  Res = 1.4991e-02\n",
            "  Iteration   2:  Res = 1.0357e-05\n",
            "  Iteration   3:  Res = 4.8587e-12\n",
            "\n",
            "Time step 26: Time 175.48 -> 182.50 days\n",
            "  Iteration   1:  Res = 1.4613e-02\n",
            "  Iteration   2:  Res = 9.8523e-06\n",
            "\n",
            "Time step 27: Time 182.50 -> 189.52 days\n",
            "  Iteration   1:  Res = 1.4244e-02\n",
            "  Iteration   2:  Res = 9.3705e-06\n",
            "\n",
            "Time step 28: Time 189.52 -> 196.54 days\n",
            "  Iteration   1:  Res = 1.3882e-02\n",
            "  Iteration   2:  Res = 8.9105e-06\n",
            "\n",
            "Time step 29: Time 196.54 -> 203.56 days\n",
            "  Iteration   1:  Res = 1.3529e-02\n",
            "  Iteration   2:  Res = 8.4715e-06\n",
            "\n",
            "Time step 30: Time 203.56 -> 210.58 days\n",
            "  Iteration   1:  Res = 1.3183e-02\n",
            "  Iteration   2:  Res = 8.0525e-06\n",
            "\n",
            "Time step 31: Time 210.58 -> 217.60 days\n",
            "  Iteration   1:  Res = 1.2845e-02\n",
            "  Iteration   2:  Res = 7.6528e-06\n",
            "\n",
            "Time step 32: Time 217.60 -> 224.62 days\n",
            "  Iteration   1:  Res = 1.2515e-02\n",
            "  Iteration   2:  Res = 7.2716e-06\n",
            "\n",
            "Time step 33: Time 224.62 -> 231.63 days\n",
            "  Iteration   1:  Res = 1.2193e-02\n",
            "  Iteration   2:  Res = 6.9082e-06\n",
            "\n",
            "Time step 34: Time 231.63 -> 238.65 days\n",
            "  Iteration   1:  Res = 1.1878e-02\n",
            "  Iteration   2:  Res = 6.5619e-06\n",
            "\n",
            "Time step 35: Time 238.65 -> 245.67 days\n",
            "  Iteration   1:  Res = 1.1570e-02\n",
            "  Iteration   2:  Res = 6.2318e-06\n",
            "\n",
            "Time step 36: Time 245.67 -> 252.69 days\n",
            "  Iteration   1:  Res = 1.1269e-02\n",
            "  Iteration   2:  Res = 5.9174e-06\n",
            "\n",
            "Time step 37: Time 252.69 -> 259.71 days\n",
            "  Iteration   1:  Res = 1.0976e-02\n",
            "  Iteration   2:  Res = 5.6180e-06\n",
            "\n",
            "Time step 38: Time 259.71 -> 266.73 days\n",
            "  Iteration   1:  Res = 1.0689e-02\n",
            "  Iteration   2:  Res = 5.3328e-06\n",
            "\n",
            "Time step 39: Time 266.73 -> 273.75 days\n",
            "  Iteration   1:  Res = 1.0409e-02\n",
            "  Iteration   2:  Res = 5.0614e-06\n",
            "\n",
            "Time step 40: Time 273.75 -> 280.77 days\n",
            "  Iteration   1:  Res = 1.0136e-02\n",
            "  Iteration   2:  Res = 4.8030e-06\n",
            "\n",
            "Time step 41: Time 280.77 -> 287.79 days\n",
            "  Iteration   1:  Res = 9.8693e-03\n",
            "  Iteration   2:  Res = 4.5572e-06\n",
            "\n",
            "Time step 42: Time 287.79 -> 294.81 days\n",
            "  Iteration   1:  Res = 9.6090e-03\n",
            "  Iteration   2:  Res = 4.3233e-06\n",
            "\n",
            "Time step 43: Time 294.81 -> 301.83 days\n",
            "  Iteration   1:  Res = 9.3551e-03\n",
            "  Iteration   2:  Res = 4.1008e-06\n",
            "\n",
            "Time step 44: Time 301.83 -> 308.85 days\n",
            "  Iteration   1:  Res = 9.1073e-03\n",
            "  Iteration   2:  Res = 3.8893e-06\n",
            "\n",
            "Time step 45: Time 308.85 -> 315.87 days\n",
            "  Iteration   1:  Res = 8.8655e-03\n",
            "  Iteration   2:  Res = 3.6881e-06\n",
            "\n",
            "Time step 46: Time 315.87 -> 322.88 days\n",
            "  Iteration   1:  Res = 8.6296e-03\n",
            "  Iteration   2:  Res = 3.4969e-06\n",
            "\n",
            "Time step 47: Time 322.88 -> 329.90 days\n",
            "  Iteration   1:  Res = 8.3996e-03\n",
            "  Iteration   2:  Res = 3.3152e-06\n",
            "\n",
            "Time step 48: Time 329.90 -> 336.92 days\n",
            "  Iteration   1:  Res = 8.1752e-03\n",
            "  Iteration   2:  Res = 3.1426e-06\n",
            "\n",
            "Time step 49: Time 336.92 -> 343.94 days\n",
            "  Iteration   1:  Res = 7.9564e-03\n",
            "  Iteration   2:  Res = 2.9785e-06\n",
            "\n",
            "Time step 50: Time 343.94 -> 350.96 days\n",
            "  Iteration   1:  Res = 7.7430e-03\n",
            "  Iteration   2:  Res = 2.8227e-06\n",
            "\n",
            "Time step 51: Time 350.96 -> 357.98 days\n",
            "  Iteration   1:  Res = 7.5350e-03\n",
            "  Iteration   2:  Res = 2.6747e-06\n",
            "\n",
            "Time step 52: Time 357.98 -> 365.00 days\n",
            "  Iteration   1:  Res = 7.3322e-03\n",
            "  Iteration   2:  Res = 2.5342e-06\n",
            "ans = 1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "id": "slDxoZCUHJOK",
        "outputId": "0edb8172-933b-4c16-e5b5-8b1a949d05ee"
      },
      "source": [
        "# Specify dimension\n",
        "dimension = (nx,ny,nz)\n",
        "\n",
        "# Input for plots\n",
        "plane, position = \"xy\", 2\n",
        "\n",
        "numSteps, totTime = 52, 365 # day\n",
        "steps = [2, 5, 10, 20]\n",
        "\n",
        "# Plot pressure for every timestep\n",
        "plt.figure(figsize=(10,8))\n",
        "\n",
        "for i in range(len(steps)):\n",
        "  directory = \"/content/result_gas_1phase/\"\n",
        "  filename = \"pressure{}.mat\".format(steps[i])\n",
        "\n",
        "  # Get cell data \n",
        "  cube = pymrst.getCellData(directory, filename, dimension=(nx,ny,nz))\n",
        "\n",
        "  # Convert to barsa\n",
        "  cube = cube/barsa()\n",
        "\n",
        "  # Plot for every timestep\n",
        "  day = totTime / numSteps * steps[i]\n",
        "  plt.subplot(2,2,i+1)\n",
        "  pymrst.plotCellData(cube, plane, position, cmap=\"plasma\")\n",
        "  plt.title(\"Pressure on {} Plane at Slice {} (Day {:.1f})\".format(plane, position, day),\n",
        "            size=15, pad=15)  \n",
        "\n",
        "plt.tight_layout(1.7)\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}